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      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "FXAUmr7oLSK-"
      },
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"Corona_NLP_train.csv\",encoding=\"latin-1\")"
      ],
      "metadata": {
        "id": "HNFI3hjSnaU1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "tvgYgwGIoDeO",
        "outputId": "2cdeb0f2-bf1e-41bb-ddce-2f1c7b1b8338"
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      "execution_count": 19,
      "outputs": [
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              "   UserName  ScreenName   Location     TweetAt  \\\n",
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              "2      3801       48753  Vagabonds  16-03-2020   \n",
              "3      3802       48754        NaN  16-03-2020   \n",
              "4      3803       48755        NaN  16-03-2020   \n",
              "\n",
              "                                       OriginalTweet           Sentiment  \n",
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              "2  Coronavirus Australia: Woolworths to give elde...            Positive  \n",
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              "          const element = document.querySelector('#df-164fe9ab-3c8a-46c6-a914-4568d339ab88');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "code",
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      "outputs": [
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          "data": {
            "text/plain": [
              "(41157, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
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        "id": "Ai9ETRaboHGe",
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      "execution_count": 21,
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              "       UserName  ScreenName                      Location     TweetAt  \\\n",
              "41152     44951       89903  Wellington City, New Zealand  14-04-2020   \n",
              "41153     44952       89904                           NaN  14-04-2020   \n",
              "41154     44953       89905                           NaN  14-04-2020   \n",
              "41155     44954       89906                           NaN  14-04-2020   \n",
              "41156     44955       89907  i love you so much || he/him  14-04-2020   \n",
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              "41152  Airline pilots offering to stock supermarket s...             Neutral  \n",
              "41153  Response to complaint not provided citing COVI...  Extremely Negative  \n",
              "41154  You know itÂs getting tough when @KameronWild...            Positive  \n",
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              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4fc97bed-7b18-4a4a-b44c-1820ea429be8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fc97bed-7b18-4a4a-b44c-1820ea429be8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sFt-i2dHoIsm",
        "outputId": "cba92703-e825-42c2-ca46-e20fefd8eed7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           UserName    ScreenName\n",
              "count  41157.000000  41157.000000\n",
              "mean   24377.000000  69329.000000\n",
              "std    11881.146851  11881.146851\n",
              "min     3799.000000  48751.000000\n",
              "25%    14088.000000  59040.000000\n",
              "50%    24377.000000  69329.000000\n",
              "75%    34666.000000  79618.000000\n",
              "max    44955.000000  89907.000000"
            ],
            "text/html": [
              "\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UserName</th>\n",
              "      <th>ScreenName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41157.000000</td>\n",
              "      <td>41157.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>24377.000000</td>\n",
              "      <td>69329.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11881.146851</td>\n",
              "      <td>11881.146851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3799.000000</td>\n",
              "      <td>48751.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>14088.000000</td>\n",
              "      <td>59040.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>24377.000000</td>\n",
              "      <td>69329.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34666.000000</td>\n",
              "      <td>79618.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>44955.000000</td>\n",
              "      <td>89907.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76b06203-cc4f-4fa7-8f39-b34b50274542')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-7f60cf6f-ed67-49fc-b5a7-38d8f5859dcc\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f60cf6f-ed67-49fc-b5a7-38d8f5859dcc')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7f60cf6f-ed67-49fc-b5a7-38d8f5859dcc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-76b06203-cc4f-4fa7-8f39-b34b50274542 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-76b06203-cc4f-4fa7-8f39-b34b50274542');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCQdV6jDoLZD",
        "outputId": "0e435266-e4bf-49b9-825d-731f6f94e248"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserName          int64\n",
              "ScreenName        int64\n",
              "Location         object\n",
              "TweetAt          object\n",
              "OriginalTweet    object\n",
              "Sentiment        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1d2Uz6DoRPa",
        "outputId": "daa00d6b-71b6-4b2f-cd9b-ed4c491d7f28"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserName            0\n",
              "ScreenName          0\n",
              "Location         8590\n",
              "TweetAt             0\n",
              "OriginalTweet       0\n",
              "Sentiment           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "8QM1mDzAoVxo"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCPQtjhzo8Xd",
        "outputId": "09bec55b-ee0c-41e7-e364-85774c61d5b9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['UserName', 'ScreenName', 'Location', 'TweetAt', 'OriginalTweet',\n",
              "       'Sentiment'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(labels='UserName' ,axis=1)"
      ],
      "metadata": {
        "id": "HMVvKgsqoxfw"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(labels='ScreenName' ,axis=1)"
      ],
      "metadata": {
        "id": "6ZcN2QkHpOeG"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(labels='Location' ,axis=1)"
      ],
      "metadata": {
        "id": "9V7_hkf8pS0I"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(labels='TweetAt' ,axis=1)"
      ],
      "metadata": {
        "id": "77ih16-kpYnR"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=df[\"OriginalTweet\"]\n",
        "y=df[\"Sentiment\"]"
      ],
      "metadata": {
        "id": "EwuH3uJNp513"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "rfM-1LuRqLLB"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "textclf=Pipeline([(\"tfidf\",TfidfVectorizer()),(\"clf\",LinearSVC())])"
      ],
      "metadata": {
        "id": "GlGZ7TPNqwaB"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "textclf.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "2smTS-L1rNc7",
        "outputId": "3190667d-1f5f-49ef-e3b1-615dc290628c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, LinearSVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=textclf.predict(x_test)"
      ],
      "metadata": {
        "id": "cfaRbqH8reS-"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ry9DW4nnrl40",
        "outputId": "1a532fc1-7a10-4b73-cb57-3f5807e27f3f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Positive', 'Extremely Negative', 'Extremely Positive', ...,\n",
              "       'Positive', 'Extremely Positive', 'Negative'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt=\"d\", cmap=\"Blues\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "_IlTfFI8roYe",
        "outputId": "6006c861-c48b-436a-df65-7ac40b616caf"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}